Frontiers of Information Technology & Electronic Engineering
>> 2021,
Volume 22,
Issue 9
doi:
10.1631/FITEE.2000234
Learning-based parameter prediction for quality control in three-dimensional medical image compression
Affiliation(s): State Key Lab of CAD & CG, Zhejiang University, Hangzhou 310058, China; The First Affiliated Hospital, Zhejiang University, Hangzhou 310003, China; less
Received: 2020-05-16
Accepted: 2021-09-10
Available online: 2021-09-10
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Abstract
is of vital importance in compressing three-dimensional (3D) medical imaging data. Optimal compression parameters need to be determined based on the specific quality requirement. In , regarded as the state-of-the-art compression tool, the quantization parameter (QP) plays a dominant role in controlling quality. The direct application of a video-based scheme in predicting the ideal parameters for 3D cannot guarantee satisfactory results. In this paper we propose a parameter prediction scheme to achieve efficient . Its kernel is a support vector regression (SVR) based learning model that is capable of predicting the optimal QP from both video-based and structural image features extracted directly from raw data, avoiding time-consuming processes such as pre-encoding and iteration, which are often needed in existing techniques. Experimental results on several datasets verify that our approach outperforms current video-based methods.
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